PrIntMap-R: An Online Application for Intraprotein Intensity and Peptide Visualization from Bottom-Up Proteomics

Bottom-up proteomics (BUP) produces rich data, but visualization and analysis are time-consuming and often require programming skills. Many tools analyze these data at the proteome-level, but fewer options exist for individual proteins. Sequence coverage maps are common, but do not proportion peptide intensity. Abundance-based visualization of sequence coverage facilitates detection of protein isoforms, domains, potential truncation sites, peptide “hot-spots”, and localization of post-translational modifications (PTMs). Redundant stacked-sequence coverage is an important tool in designing hydrogen–deuterium exchange (HDX) experiments. Visualization tools often lack graphical and tabular-export of processed data which complicates publication of results. Quantitative peptide abundance across amino acid sequences is an essential and missing tool in proteomics toolkits. Here we created PrIntMap-R, an online application that only requires peptide files from a database search and FASTA protein sequences. PrIntMap-R produces a variety of plots for quantitative visualization of coverage; annotation of specific sequences, PTM’s, and comparisons of one or many samples overlaid with calculated fold-change or several intensity metrics. We show use-cases including protein phosphorylation, identification of glycosylation, and the optimization of digestion conditions for HDX experiments. PrIntMap-R is freely available, open source, and can run online with no installation, or locally by downloading source code from GitHub.


Table of Contents
Samples: One injection for each of 9 conditions was analyzed.Each condition consisted of one of the following flow rates (100, 200, or 300 µL/min) and one of the following digestion times (60, 120, or 180 seconds) with all of the possible permutations analyzed.For comparisons for only one of the variables, the three injections with the same condition for that variable were combined and averaged in PrIntMap-R.

No PO4ase:
With PO4ase: There were multiple different Salmonella infection times included within these data, but for the purpose of this demonstration, all of the infection times were combined in PrIntMap-R after being searched individually in MSFragger, so that the variable investigated was the PO4ase treatment.
1.) SI Figure 1: Analysis of example fusion protein Beta-Galactosidase/Bovine Serum Albumin.2.) SI Figure 2: Additional PrIntMap-R features for glycosylation analysis of P01009.3.) SI Figure 3: Multi-sample comparison in PrIntMap-R for optimization of HDX-MS experiments.4.) Supplementary Methods and Search Parameters a.) glyco analysis in PEAKS b.) HDX-MS optimization in PEAKS c.) Phosphopeptides in MSFragger d.) Example Fusion Protein in PEAKS SI Figure 1: Analysis of example fusion protein β-Galactosidase/Bovine Serum Albumin.Digestion of (A) E. coli β-Galactosidase (BGal) and (B) Bovine Serum Albumin (BSA) each searched against a database containing a fusion protein containing the Gal amino acid sequence at the N-terminus and the BSA amino acid sequence at the C-terminus.PrIntMap-R was used to map the resulting LFQ Area onto the amino acid sequence of this in-silico artificial fusion protein, showing the difference in intensity at different domains.SI Figure 2: Additional PrIntMap-R features for glycosylation analysis of P01009.(A) Volcano plot showing all the peptides identified.Log2 fold change is based on Deglycosylated / Control.Infinite values shown in dotted boxes at the top corners of the plot.Green points are peptides that were mapped to the protein of interest (P01009), black points are peptides that fall above the significance and fold change thresholds, and gray points are peptides that do not.(B) Example of popup with extra information when 'mousing' over one of the points in the volcano plot.(C) Unique peptide plot, pink regions were identified peptides that mapped to elsewhere in the proteome database, while blue regions were unique to P01009.(D) Example of popup with extra information when 'mousing' over one of the points in the unique peptide plot.Data from PXD09721.SI Figure 3: Multi-sample comparison in PrIntMap-R for optimization of HDX-MS experiments.(A) Comparison of three experimental conditions for flow rate: Orange: 100 µL/min, Green: 200 µL/min, Blue: 300 µL/min, with observed area on the y-axis.(B) Nine-sample comparison for each experimental condition (flow rate and time), with observed area on the yaxis.(C) Nine-sample area fold change comparison for each experimental condition, where the fold change is based on the 200 µL/min, 120 second sample.
d) BSA and Beta-Galactosidase Example Fusion Protein Search Parameters in PEAKS Search Parameters: Precursor Mass Error Tolerance: 20 ppm Fragment Mass Error Tolerance: 0.05 Da

) Search parameters for HX-MS optimization in PEAKS
Specify amino acids on which delta masses (mass offsets or search modifications) can occur.Allowed values are single letter codes (e.g.ACD) and '-', must be capitalized.Use 'all' to allow any amino acid.diagnostic_intensity_filter = 0 # [nglycan/labile search_mode only].Minimum relative intensity for SUM of all detected oxonium ions to achieve for spectrum to contain diagnostic fragment evidence.Calculated relative to spectrum base peak.0 <= value.
Files downloaded from: https://massive.ucsd.edu/ProteoSAFe/dataset.# Ion series used in search, specifyany of a,b,c,x,y,z,Y,b-18,y-18 (comma separated).# Track top N unmodified peptide results separately from main results internally for boosting features.zero_bin_accept_expect = 0 # Ranks a zero-bin hit above all non-zero-bin hit if it has expectation less than this value.zero_bin_mult_expect = 1 # Multiplies expect value of PSMs in the zero-bin during results ordering (set to less than 1 for boosting).add_topN_complementary = 0 # Inserts complementary ions corresponding to the top N most intense fragments in each experimental spectra.